LinkMirage: Enabling Privacy-preserving Analytics on Social Relationships

Social relationships present a critical foundation for many real-world applications. However, both users and online social network (OSN) providers are hesitant to share social relationships with untrusted external applications due to privacy concerns. In this work, we design LinkMirage, a system that mediates privacy-preserving access to social relationships. LinkMirage takes users' social relationship graph as an input, obfuscates the social graph topology, and provides untrusted external applications with an obfuscated view of the social relationship graph while preserving graph utility. Our key contributions are (1) a novel algorithm for obfuscating social relationship graph while preserving graph utility, (2) theoretical and experimental analysis of privacy and utility using real-world social network topologies, including a large-scale Google+ dataset with 940 million links. Our experimental results demonstrate that LinkMirage provides up to 10x improvement in privacy guarantees compared to the state-of-the-art approaches. Overall, LinkMirage enables the design of real-world applications such as recommendation systems, graph analytics, anonymous communications, and Sybil defenses while protecting the privacy of social relationships. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment. NDSS '15,